ANCOVA in flow (textual prod) (flow (textual prod))

Geiser C. Challco geiser@alumni.usp.br

NOTE:

Setting Initial Variables

dv = "flow.text"
dv.pos = "fss.media.text"
dv.pre = "dfs.media.text"

fatores2 <- c("Sexo","Zona","Cor.Raca","Serie")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2

fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1

lfatores <- c(lfatores1)

color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
  "Parda"="#b97100","Indígena"="#9F262F",
  "Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)

level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")

# ..

ymin <- 0
ymax <- 0

ymin.ci <- 0
ymax.ci <- 0


color[["grupo:Sexo"]] = c(
  "Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
  "Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
  "Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
  "Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
  "Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
  "Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
  "Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
  "Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
    "Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
    "Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
    "Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
    "Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
    "Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
    
    "Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
    "Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
    "Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
    "Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
    "Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)


for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
               "TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
               "leitura.compreensao")) {
  color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
  level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
  color[[paste0("grupo:",coln,".quintile")]] = c(
    "Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
    "Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
    "Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
    "Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
    "Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
    
    "Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
    "Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
    "Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
    "Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
    "Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}


gdat <- read_excel("../data/data.xlsx", sheet = "sumary")
gdat <- gdat[which(is.na(gdat$Necessidade.Deficiencia) & !is.na(gdat$Stari.Grupo)),]



dat <- gdat
dat$grupo <- factor(dat[["Stari.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
  dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]

dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])


for (f in c("grupo", names(lfatores))) {
  if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0) 
      color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
  if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
    color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}

ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()

Descriptive Statistics of Initial Data

df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"), 
                       include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
  df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
    if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
      get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
                       symmetry.test = T, normality.test = F)
    }))
)
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
## There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
            colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
grupo Sexo Zona Cor.Raca Serie variable n mean median min max sd se ci iqr symmetry skewness kurtosis
Controle dfs.media.text 94 3.345 3.333 1.333 4.778 0.572 0.059 0.117 0.618 YES -0.490 0.954
Experimental dfs.media.text 44 3.472 3.444 1.889 4.444 0.575 0.087 0.175 0.694 YES -0.208 -0.173
dfs.media.text 138 3.385 3.389 1.333 4.778 0.574 0.049 0.097 0.667 YES -0.397 0.682
Controle fss.media.text 94 3.491 3.556 1.889 5.000 0.594 0.061 0.122 0.889 YES -0.121 -0.341
Experimental fss.media.text 44 3.380 3.354 2.333 4.667 0.586 0.088 0.178 0.833 YES 0.111 -0.751
fss.media.text 138 3.456 3.444 1.889 5.000 0.591 0.050 0.100 0.889 YES -0.045 -0.462
Controle F dfs.media.text 42 3.368 3.333 2.333 4.444 0.500 0.077 0.156 0.444 YES 0.102 -0.362
Controle M dfs.media.text 52 3.326 3.389 1.333 4.778 0.628 0.087 0.175 0.778 NO -0.677 0.956
Experimental F dfs.media.text 15 3.289 3.333 1.889 4.444 0.686 0.177 0.380 0.778 YES -0.160 -0.697
Experimental M dfs.media.text 29 3.567 3.444 2.778 4.444 0.495 0.092 0.188 0.667 YES 0.222 -1.078
Controle F fss.media.text 42 3.612 3.722 2.444 4.778 0.583 0.090 0.182 0.771 YES -0.200 -0.716
Controle M fss.media.text 52 3.393 3.444 1.889 5.000 0.589 0.082 0.164 0.889 YES -0.057 -0.091
Experimental F fss.media.text 15 3.469 3.375 2.333 4.556 0.648 0.167 0.359 1.000 YES -0.075 -1.230
Experimental M fss.media.text 29 3.334 3.333 2.333 4.667 0.558 0.104 0.212 0.778 YES 0.181 -0.550
Controle Rural dfs.media.text 51 3.345 3.333 2.111 4.778 0.577 0.081 0.162 0.667 YES 0.201 -0.340
Controle Urbana dfs.media.text 15 3.020 3.222 1.333 4.000 0.740 0.191 0.410 0.722 NO -0.797 -0.424
Controle dfs.media.text 28 3.518 3.472 2.778 4.222 0.369 0.070 0.143 0.556 YES 0.101 -0.975
Experimental Rural dfs.media.text 29 3.494 3.556 1.889 4.444 0.625 0.116 0.238 0.778 YES -0.386 -0.304
Experimental Urbana dfs.media.text 6 3.444 3.444 2.889 4.333 0.544 0.222 0.571 0.639 YES 0.383 -1.448
Experimental dfs.media.text 9 3.420 3.444 2.667 4.333 0.464 0.155 0.357 0.111 YES 0.340 -0.447
Controle Rural fss.media.text 51 3.475 3.556 2.444 4.778 0.581 0.081 0.164 0.889 YES 0.033 -0.903
Controle Urbana fss.media.text 15 3.511 3.333 1.889 5.000 0.791 0.204 0.438 1.000 YES 0.036 -0.602
Controle fss.media.text 28 3.508 3.667 2.333 4.222 0.512 0.097 0.198 0.667 NO -0.824 -0.465
Experimental Rural fss.media.text 29 3.389 3.333 2.333 4.667 0.607 0.113 0.231 0.778 YES 0.256 -0.729
Experimental Urbana fss.media.text 6 3.377 3.299 2.889 4.111 0.453 0.185 0.476 0.538 YES 0.439 -1.520
Experimental fss.media.text 9 3.355 3.444 2.333 4.111 0.655 0.218 0.504 1.139 YES -0.361 -1.660
Controle Parda dfs.media.text 45 3.421 3.333 2.444 4.444 0.466 0.069 0.140 0.556 YES 0.146 -0.364
Controle Branca dfs.media.text 9 3.173 3.222 2.111 4.222 0.674 0.225 0.518 0.667 YES -0.132 -1.303
Controle Preta dfs.media.text 1 3.222 3.222 3.222 3.222 0.000 few data 0.000 0.000
Controle dfs.media.text 39 3.300 3.333 1.333 4.778 0.661 0.106 0.214 0.764 NO -0.618 0.775
Experimental Parda dfs.media.text 12 3.426 3.278 2.444 4.444 0.599 0.173 0.381 0.861 YES 0.177 -1.243
Experimental Indígena dfs.media.text 5 3.711 3.667 3.222 4.333 0.405 0.181 0.503 0.222 YES 0.362 -1.460
Experimental Branca dfs.media.text 6 3.593 3.500 2.889 4.444 0.529 0.216 0.555 0.444 YES 0.310 -1.330
Experimental Amarela dfs.media.text 1 3.444 3.444 3.444 3.444 0.000 few data 0.000 0.000
Experimental dfs.media.text 20 3.406 3.444 1.889 4.333 0.637 0.142 0.298 0.806 YES -0.299 -0.370
Controle Parda fss.media.text 45 3.580 3.667 2.444 5.000 0.589 0.088 0.177 0.778 YES 0.122 -0.439
Controle Branca fss.media.text 9 3.358 3.556 2.556 4.000 0.538 0.179 0.414 0.778 YES -0.273 -1.734
Controle Preta fss.media.text 1 4.000 4.000 4.000 4.000 0.000 few data 0.000 0.000
Controle fss.media.text 39 3.405 3.444 1.889 4.556 0.609 0.098 0.197 0.889 YES -0.318 -0.593
Experimental Parda fss.media.text 12 3.130 3.111 2.333 4.111 0.556 0.161 0.353 0.861 YES 0.201 -1.374
Experimental Indígena fss.media.text 5 3.675 3.444 3.222 4.222 0.458 0.205 0.568 0.736 YES 0.239 -2.185
Experimental Branca fss.media.text 6 3.444 3.444 2.889 3.889 0.391 0.160 0.411 0.528 YES -0.160 -1.905
Experimental Amarela fss.media.text 1 3.889 3.889 3.889 3.889 0.000 few data 0.000 0.000
Experimental fss.media.text 20 3.413 3.354 2.333 4.667 0.660 0.148 0.309 0.972 YES 0.228 -0.980
Controle 6 ano dfs.media.text 31 3.269 3.333 2.000 4.778 0.665 0.119 0.244 0.500 YES -0.061 -0.322
Controle 7 ano dfs.media.text 29 3.383 3.333 2.556 4.333 0.449 0.083 0.171 0.667 YES 0.227 -0.711
Controle 8 ano dfs.media.text 15 3.219 3.111 1.333 4.222 0.686 0.177 0.380 0.417 NO -1.023 1.430
Controle 9 ano dfs.media.text 19 3.510 3.556 2.444 4.222 0.470 0.108 0.227 0.611 YES -0.441 -0.608
Experimental 6 ano dfs.media.text 15 3.600 3.667 1.889 4.444 0.717 0.185 0.397 1.000 NO -0.722 -0.262
Experimental 7 ano dfs.media.text 11 3.505 3.444 2.889 4.444 0.534 0.161 0.359 0.778 YES 0.240 -1.441
Experimental 8 ano dfs.media.text 9 3.395 3.444 2.444 4.333 0.591 0.197 0.454 0.444 YES 0.043 -1.170
Experimental 9 ano dfs.media.text 9 3.296 3.333 2.667 3.667 0.309 0.103 0.238 0.333 NO -0.714 -0.660
Controle 6 ano fss.media.text 31 3.488 3.556 1.889 4.556 0.595 0.107 0.218 0.778 NO -0.554 -0.027
Controle 7 ano fss.media.text 29 3.586 3.667 2.444 5.000 0.686 0.127 0.261 0.889 YES 0.085 -0.946
Controle 8 ano fss.media.text 15 3.304 3.333 2.333 4.000 0.480 0.124 0.266 0.667 YES -0.402 -0.899
Controle 9 ano fss.media.text 19 3.497 3.556 2.556 4.222 0.526 0.121 0.253 0.889 YES -0.284 -1.332
Experimental 6 ano fss.media.text 15 3.648 3.375 3.000 4.667 0.521 0.134 0.288 0.722 NO 0.631 -1.000
Experimental 7 ano fss.media.text 11 3.448 3.444 2.556 4.111 0.513 0.155 0.345 0.535 NO -0.623 -0.963
Experimental 8 ano fss.media.text 9 2.972 2.778 2.333 4.111 0.551 0.184 0.423 0.667 NO 0.781 -0.624
Experimental 9 ano fss.media.text 9 3.259 3.222 2.333 4.222 0.629 0.210 0.483 0.778 YES 0.121 -1.515

ANCOVA and Pairwise for one factor: grupo

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "fss.media.text", "grupo")

pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.text"]] <- c(pdat[["dfs.media.text"]], pdat[["fss.media.text"]])

aov = anova_test(pdat, fss.media.text ~ dfs.media.text + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, fss.media.text ~ grupo, covariate = dfs.media.text,
                    p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
                          flow.text ~ time,
                          p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "fss.media.text", "grupo", covar = "dfs.media.text")
ds <- merge(ds[ds$variable != "dfs.media.text",],
            ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
            by = "grupo", all.x = T, suffixes = c("", ".dfs.media.text"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.media.text","se.dfs.media.text","mean","se",
            "emmean","se.emms","conf.low","conf.high")]

colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
                  paste0(c("M","SE"), " (unadj)"),
                  paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")

lemms[["grupo"]] <- ds

Computing ANCOVA and PairWise After removing non-normal data (OK)

wdat = pdat 

res = residuals(lm(fss.media.text ~ dfs.media.text + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)

wdat = wdat[!wdat$id %in% non.normal,]

wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.text"]] <- c(wdat[["dfs.media.text"]], wdat[["fss.media.text"]])

ldat[["grupo"]] = wdat

(non.normal)
## NULL
aov = anova_test(wdat, fss.media.text ~ dfs.media.text + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
                            by="Effect", suffixes = c("","'"))

(df = get_anova_table(aov))
## ANOVA Table (type II tests)
## 
##           Effect DFn DFd      F        p p<.05   ges
## 1 dfs.media.text   1 135 19.051 2.51e-05     * 0.124
## 2          grupo   1 135  2.361 1.27e-01       0.017
Effect DFn DFd F p p<.05 ges
dfs.media.text 1 135 19.051 0.000 * 0.124
grupo 1 135 2.361 0.127 0.017 
pwc <- emmeans_test(wdat, fss.media.text ~ grupo, covariate = dfs.media.text,
                    p.adjust.method = "bonferroni")
term .y. group1 group2 df statistic p p.adj p.adj.signif
dfs.media.text*grupo fss.media.text Controle Experimental 135 1.537 0.127 0.127 ns 
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
                         flow.text ~ time,
                         p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
                            by=c("grupo","term",".y.","group1","group2"),
                            suffixes = c("","'"))
grupo term .y. group1 group2 df statistic p p.adj p.adj.signif
Controle time flow.text pre pos 272 -1.721 0.086 0.086 ns
Experimental time flow.text pre pos 272 0.740 0.460 0.460 ns 
ds <- get.descriptives(wdat, "fss.media.text", "grupo", covar = "dfs.media.text")
ds <- merge(ds[ds$variable != "dfs.media.text",],
            ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
            by = "grupo", all.x = T, suffixes = c("", ".dfs.media.text"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.media.text","se.dfs.media.text","mean","se",
            "emmean","se.emms","conf.low","conf.high")]

colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
                  paste0(c("M","SE"), " (unadj)"),
                  paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")

lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
grupo N M (pre) SE (pre) M (unadj) SE (unadj) M (adj) SE (adj) conf.low conf.high
Controle 94 3.345 0.059 3.491 0.061 3.506 0.057 3.392 3.619
Experimental 44 3.472 0.087 3.380 0.088 3.349 0.084 3.183 3.515

Plots for ancova

plots <- oneWayAncovaPlots(
  wdat, "fss.media.text", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
  font.label.size=10, step.increase=0.05, p.label="p.adj",
  subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
  plots[["grupo"]] +
  if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
  wdat, "fss.media.text", "grupo", aov, pwc, covar = "dfs.media.text",
  theme = "classic", color = color[["grupo"]],
  subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
  plots[["grupo"]] + ggplot2::ylab("flow (textual prod)") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
  plots <- oneWayAncovaBoxPlots(
    wdat.long, "flow.text", "grupo", aov, pwc.long,
    pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
  plots[["grupo"]] + ggplot2::ylab("flow (textual prod)") +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
          color = "grupo", add = "reg.line")+
  stat_regline_equation(
    aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
  ) +
  ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
  ggplot2::scale_color_manual(values = color[["grupo"]]) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and homogeneity

res <- augment(lm(fss.media.text ~ dfs.media.text + grupo, data = wdat))
shapiro_test(res$.resid)
## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.991   0.507
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     1   136   0.00832 0.927

ANCOVA and Pairwise for two factors grupo:Sexo

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
                         "fss.media.text", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
  pdat[["Sexo"]],
  level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.text"]] <- c(pdat[["dfs.media.text"]], pdat[["fss.media.text"]])

if (length(unique(pdat[["Sexo"]])) >= 2) {
  aov = anova_test(pdat, fss.media.text ~ dfs.media.text + grupo*Sexo)
  laov[["grupo:Sexo"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwcs <- list()
  pwcs[["Sexo"]] <- emmeans_test(
    group_by(pdat, grupo), fss.media.text ~ Sexo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Sexo), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
  pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
  ds <- get.descriptives(pdat, "fss.media.text", c("grupo","Sexo"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Sexo"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Sexo","n","mean.dfs.media.text","se.dfs.media.text","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Sexo"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Sexo"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(fss.media.text ~ dfs.media.text + grupo*Sexo, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["flow.text"]] <- c(wdat[["dfs.media.text"]], wdat[["fss.media.text"]])
  
  
  ldat[["grupo:Sexo"]] = wdat
  
  (non.normal)
}
## NULL
if (length(unique(pdat[["Sexo"]])) >= 2) {
  aov = anova_test(wdat, fss.media.text ~ dfs.media.text + grupo*Sexo)
  laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}
Effect DFn DFd F p p<.05 ges
dfs.media.text 1 133 20.216 0.000 * 0.132
grupo 1 133 1.780 0.184 0.013
Sexo 1 133 4.986 0.027 * 0.036
grupo:Sexo 1 133 0.030 0.863 0.000 
if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwcs <- list()
  pwcs[["Sexo"]] <- emmeans_test(
    group_by(wdat, grupo), fss.media.text ~ Sexo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Sexo), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
  pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
grupo Sexo term .y. group1 group2 df statistic p p.adj p.adj.signif
F dfs.media.text*grupo fss.media.text Controle Experimental 133 0.681 0.497 0.497 ns
M dfs.media.text*grupo fss.media.text Controle Experimental 133 1.156 0.250 0.250 ns
Controle dfs.media.text*Sexo fss.media.text F M 133 1.777 0.078 0.078 ns
Experimental dfs.media.text*Sexo fss.media.text F M 133 1.356 0.177 0.177 ns 
if (length(unique(pdat[["Sexo"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Sexo"]],
                                         by=c("grupo","Sexo","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}
grupo Sexo term .y. group1 group2 df statistic p p.adj p.adj.signif
Controle F time flow.text pre pos 268 -1.924 0.055 0.055 ns
Controle M time flow.text pre pos 268 -0.594 0.553 0.553 ns
Experimental F time flow.text pre pos 268 -0.853 0.394 0.394 ns
Experimental M time flow.text pre pos 268 1.529 0.127 0.127 ns 
if (length(unique(pdat[["Sexo"]])) >= 2) {
  ds <- get.descriptives(wdat, "fss.media.text", c("grupo","Sexo"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Sexo"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Sexo","n","mean.dfs.media.text","se.dfs.media.text",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
                                          by=c("grupo","Sexo"), suffixes = c("","'"))
}
grupo Sexo N M (pre) SE (pre) M (unadj) SE (unadj) M (adj) SE (adj) conf.low conf.high
Controle F 42 3.368 0.077 3.612 0.090 3.618 0.085 3.450 3.786
Controle M 52 3.326 0.087 3.393 0.082 3.415 0.076 3.264 3.566
Experimental F 15 3.289 0.177 3.469 0.167 3.505 0.142 3.224 3.786
Experimental M 29 3.567 0.092 3.334 0.104 3.266 0.103 3.063 3.470

Plots for ancova

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "flow (textual prod)",
             subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Sexo"]]) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "flow (textual prod)",
               subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "fss.media.text", c("grupo","Sexo"), aov, pwcs, covar = "dfs.media.text",
    theme = "classic", color = color[["grupo:Sexo"]],
    subtitle = which(aov$Effect == "grupo:Sexo"))
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
  plots[["grupo:Sexo"]] + ggplot2::ylab("flow (textual prod)") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Sexo"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "flow.text", c("grupo","Sexo"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Sexo"]])) >= 2) 
  plots[["grupo:Sexo"]] + ggplot2::ylab("flow (textual prod)") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            facet.by = c("grupo","Sexo"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "grupo", facet.by = "Sexo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "Sexo", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
    ggplot2::scale_color_manual(values = color[["Sexo"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Sexo"]])) >= 2) 
  res <- augment(lm(fss.media.text ~ dfs.media.text + grupo*Sexo, data = wdat))
if (length(unique(pdat[["Sexo"]])) >= 2)
  shapiro_test(res$.resid)
## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.989   0.364
if (length(unique(pdat[["Sexo"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Sexo)
## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     3   134     0.348 0.791

ANCOVA and Pairwise for two factors grupo:Zona

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
                         "fss.media.text", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
  pdat[["Zona"]],
  level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.text"]] <- c(pdat[["dfs.media.text"]], pdat[["fss.media.text"]])

if (length(unique(pdat[["Zona"]])) >= 2) {
  aov = anova_test(pdat, fss.media.text ~ dfs.media.text + grupo*Zona)
  laov[["grupo:Zona"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
  pwcs <- list()
  pwcs[["Zona"]] <- emmeans_test(
    group_by(pdat, grupo), fss.media.text ~ Zona,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Zona), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
  pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
if (length(unique(pdat[["Zona"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
  ds <- get.descriptives(pdat, "fss.media.text", c("grupo","Zona"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Zona"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Zona"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Zona","n","mean.dfs.media.text","se.dfs.media.text","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Zona"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Zona"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(fss.media.text ~ dfs.media.text + grupo*Zona, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["flow.text"]] <- c(wdat[["dfs.media.text"]], wdat[["fss.media.text"]])
  
  
  ldat[["grupo:Zona"]] = wdat
  
  (non.normal)
}
## NULL
if (length(unique(pdat[["Zona"]])) >= 2) {
  aov = anova_test(wdat, fss.media.text ~ dfs.media.text + grupo*Zona)
  laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}
Effect DFn DFd F p p<.05 ges
dfs.media.text 1 96 16.264 0.000 * 0.145
grupo 1 96 1.981 0.162 0.020
Zona 1 96 0.625 0.431 0.006
grupo:Zona 1 96 0.243 0.623 0.003 
if (length(unique(pdat[["Zona"]])) >= 2) {
  pwcs <- list()
  pwcs[["Zona"]] <- emmeans_test(
    group_by(wdat, grupo), fss.media.text ~ Zona,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Zona), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
  pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
grupo Zona term .y. group1 group2 df statistic p p.adj p.adj.signif
Rural dfs.media.text*grupo fss.media.text Controle Experimental 96 1.069 0.288 0.288 ns
Urbana dfs.media.text*grupo fss.media.text Controle Experimental 96 1.055 0.294 0.294 ns
Controle dfs.media.text*Zona fss.media.text Rural Urbana 96 -0.931 0.354 0.354 ns
Experimental dfs.media.text*Zona fss.media.text Rural Urbana 96 -0.029 0.977 0.977 ns 
if (length(unique(pdat[["Zona"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Zona"]],
                                         by=c("grupo","Zona","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}
grupo Zona term .y. group1 group2 df statistic p p.adj p.adj.signif
Controle Rural time flow.text pre pos 194 -1.068 0.287 0.287 ns
Controle Urbana time flow.text pre pos 194 -2.180 0.030 0.030 *
Experimental Rural time flow.text pre pos 194 0.651 0.516 0.516 ns
Experimental Urbana time flow.text pre pos 194 0.189 0.851 0.851 ns 
if (length(unique(pdat[["Zona"]])) >= 2) {
  ds <- get.descriptives(wdat, "fss.media.text", c("grupo","Zona"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Zona"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Zona"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Zona","n","mean.dfs.media.text","se.dfs.media.text",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
                                          by=c("grupo","Zona"), suffixes = c("","'"))
}
grupo Zona N M (pre) SE (pre) M (unadj) SE (unadj) M (adj) SE (adj) conf.low conf.high
Controle Rural 51 3.345 0.081 3.475 0.081 3.476 0.080 3.316 3.635
Controle Urbana 15 3.020 0.191 3.511 0.204 3.635 0.151 3.335 3.936
Experimental Rural 29 3.494 0.116 3.389 0.113 3.332 0.108 3.119 3.546
Experimental Urbana 6 3.444 0.222 3.377 0.185 3.340 0.235 2.874 3.805

Plots for ancova

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "flow (textual prod)",
             subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Zona"]]) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "flow (textual prod)",
               subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "fss.media.text", c("grupo","Zona"), aov, pwcs, covar = "dfs.media.text",
    theme = "classic", color = color[["grupo:Zona"]],
    subtitle = which(aov$Effect == "grupo:Zona"))
}
if (length(unique(pdat[["Zona"]])) >= 2) {
  plots[["grupo:Zona"]] + ggplot2::ylab("flow (textual prod)") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Zona"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "flow.text", c("grupo","Zona"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Zona"]])) >= 2) 
  plots[["grupo:Zona"]] + ggplot2::ylab("flow (textual prod)") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            facet.by = c("grupo","Zona"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "grupo", facet.by = "Zona", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "Zona", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
    ggplot2::scale_color_manual(values = color[["Zona"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Zona"]])) >= 2) 
  res <- augment(lm(fss.media.text ~ dfs.media.text + grupo*Zona, data = wdat))
if (length(unique(pdat[["Zona"]])) >= 2)
  shapiro_test(res$.resid)
## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.992   0.795
if (length(unique(pdat[["Zona"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Zona)
## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     3    97     0.197 0.898

ANCOVA and Pairwise for two factors grupo:Cor.Raca

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
                         "fss.media.text", c("grupo","Cor.Raca"))
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
  pdat[["Cor.Raca"]],
  level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.text"]] <- c(pdat[["dfs.media.text"]], pdat[["fss.media.text"]])

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  aov = anova_test(pdat, fss.media.text ~ dfs.media.text + grupo*Cor.Raca)
  laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwcs <- list()
  pwcs[["Cor.Raca"]] <- emmeans_test(
    group_by(pdat, grupo), fss.media.text ~ Cor.Raca,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Cor.Raca), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
  pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ds <- get.descriptives(pdat, "fss.media.text", c("grupo","Cor.Raca"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Cor.Raca","n","mean.dfs.media.text","se.dfs.media.text","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Cor.Raca"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(fss.media.text ~ dfs.media.text + grupo*Cor.Raca, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["flow.text"]] <- c(wdat[["dfs.media.text"]], wdat[["fss.media.text"]])
  
  
  ldat[["grupo:Cor.Raca"]] = wdat
  
  (non.normal)
}
## NULL
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  aov = anova_test(wdat, fss.media.text ~ dfs.media.text + grupo*Cor.Raca)
  laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}
Effect DFn DFd F p p<.05 ges
dfs.media.text 1 67 9.660 0.003 * 0.126
grupo 1 67 5.625 0.021 * 0.077
Cor.Raca 1 67 0.002 0.966 0.000
grupo:Cor.Raca 1 67 1.279 0.262 0.019 
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwcs <- list()
  pwcs[["Cor.Raca"]] <- emmeans_test(
    group_by(wdat, grupo), fss.media.text ~ Cor.Raca,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Cor.Raca), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
  pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
grupo Cor.Raca term .y. group1 group2 df statistic p p.adj p.adj.signif
Parda dfs.media.text*grupo fss.media.text Controle Experimental 67 2.615 0.011 0.011 *
Branca dfs.media.text*grupo fss.media.text Controle Experimental 67 0.263 0.794 0.794 ns
Controle dfs.media.text*Cor.Raca fss.media.text Parda Branca 67 0.644 0.522 0.522 ns
Experimental dfs.media.text*Cor.Raca fss.media.text Parda Branca 67 -0.938 0.351 0.351 ns 
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Cor.Raca"]],
                                         by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}
grupo Cor.Raca term .y. group1 group2 df statistic p p.adj p.adj.signif
Controle Parda time flow.text pre pos 136 -1.390 0.167 0.167 ns
Controle Branca time flow.text pre pos 136 -0.722 0.472 0.472 ns
Experimental Parda time flow.text pre pos 136 1.333 0.185 0.185 ns
Experimental Branca time flow.text pre pos 136 0.471 0.638 0.638 ns 
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ds <- get.descriptives(wdat, "fss.media.text", c("grupo","Cor.Raca"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Cor.Raca","n","mean.dfs.media.text","se.dfs.media.text",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
                                          by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}
grupo Cor.Raca N M (pre) SE (pre) M (unadj) SE (unadj) M (adj) SE (adj) conf.low conf.high
Controle Branca 9 3.173 0.225 3.358 0.179 3.447 0.180 3.088 3.806
Controle Parda 45 3.421 0.069 3.580 0.088 3.574 0.079 3.416 3.733
Experimental Branca 6 3.593 0.216 3.444 0.160 3.372 0.219 2.936 3.809
Experimental Parda 12 3.426 0.173 3.130 0.161 3.122 0.154 2.814 3.429

Plots for ancova

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "flow (textual prod)",
             subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "flow (textual prod)",
               subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "fss.media.text", c("grupo","Cor.Raca"), aov, pwcs, covar = "dfs.media.text",
    theme = "classic", color = color[["grupo:Cor.Raca"]],
    subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  plots[["grupo:Cor.Raca"]] + ggplot2::ylab("flow (textual prod)") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "flow.text", c("grupo","Cor.Raca"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) 
  plots[["grupo:Cor.Raca"]] + ggplot2::ylab("flow (textual prod)") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
    ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Cor.Raca"]])) >= 2) 
  res <- augment(lm(fss.media.text ~ dfs.media.text + grupo*Cor.Raca, data = wdat))
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
  shapiro_test(res$.resid)
## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.980   0.314
if (length(unique(pdat[["Cor.Raca"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Cor.Raca)
## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     3    68     0.996 0.400

ANCOVA and Pairwise for two factors grupo:Serie

Without remove non-normal data

pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
                         "fss.media.text", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
  intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
    unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
  })),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
  pdat[["Serie"]],
  level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])

pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.text"]] <- c(pdat[["dfs.media.text"]], pdat[["fss.media.text"]])

if (length(unique(pdat[["Serie"]])) >= 2) {
  aov = anova_test(pdat, fss.media.text ~ dfs.media.text + grupo*Serie)
  laov[["grupo:Serie"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
  pwcs <- list()
  pwcs[["Serie"]] <- emmeans_test(
    group_by(pdat, grupo), fss.media.text ~ Serie,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(pdat, Serie), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
  pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
if (length(unique(pdat[["Serie"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
  ds <- get.descriptives(pdat, "fss.media.text", c("grupo","Serie"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Serie"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Serie"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Serie","n","mean.dfs.media.text","se.dfs.media.text","mean","se",
              "emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Serie"]] <- ds
}

Computing ANCOVA and PairWise After removing non-normal data (OK)

if (length(unique(pdat[["Serie"]])) >= 2) {
  wdat = pdat 
  
  res = residuals(lm(fss.media.text ~ dfs.media.text + grupo*Serie, data = wdat))
  non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
  
  wdat = wdat[!wdat$id %in% non.normal,]
  
  wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
  wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
  wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
  wdat.long[["flow.text"]] <- c(wdat[["dfs.media.text"]], wdat[["fss.media.text"]])
  
  
  ldat[["grupo:Serie"]] = wdat
  
  (non.normal)
}
## NULL
if (length(unique(pdat[["Serie"]])) >= 2) {
  aov = anova_test(wdat, fss.media.text ~ dfs.media.text + grupo*Serie)
  laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
                                         by="Effect", suffixes = c("","'"))
  df = get_anova_table(aov)
}
Effect DFn DFd F p p<.05 ges
dfs.media.text 1 129 16.607 0.000 * 0.114
grupo 1 129 1.939 0.166 0.015
Serie 3 129 2.167 0.095 0.048
grupo:Serie 3 129 0.797 0.498 0.018 
if (length(unique(pdat[["Serie"]])) >= 2) {
  pwcs <- list()
  pwcs[["Serie"]] <- emmeans_test(
    group_by(wdat, grupo), fss.media.text ~ Serie,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  pwcs[["grupo"]] <- emmeans_test(
    group_by(wdat, Serie), fss.media.text ~ grupo,
    covariate = dfs.media.text, p.adjust.method = "bonferroni")
  
  pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
  pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
grupo Serie term .y. group1 group2 df statistic p p.adj p.adj.signif
6 ano dfs.media.text*grupo fss.media.text Controle Experimental 129 -0.267 0.790 0.790 ns
7 ano dfs.media.text*grupo fss.media.text Controle Experimental 129 0.921 0.359 0.359 ns
8 ano dfs.media.text*grupo fss.media.text Controle Experimental 129 1.687 0.094 0.094 ns
9 ano dfs.media.text*grupo fss.media.text Controle Experimental 129 0.740 0.461 0.461 ns
Controle dfs.media.text*Serie fss.media.text 6 ano 7 ano 129 -0.413 0.680 1.000 ns
Controle dfs.media.text*Serie fss.media.text 6 ano 8 ano 129 0.969 0.335 1.000 ns
Controle dfs.media.text*Serie fss.media.text 6 ano 9 ano 129 0.455 0.650 1.000 ns
Controle dfs.media.text*Serie fss.media.text 7 ano 8 ano 129 1.291 0.199 1.000 ns
Controle dfs.media.text*Serie fss.media.text 7 ano 9 ano 129 0.814 0.417 1.000 ns
Controle dfs.media.text*Serie fss.media.text 8 ano 9 ano 129 -0.491 0.624 1.000 ns
Experimental dfs.media.text*Serie fss.media.text 6 ano 7 ano 129 0.767 0.444 1.000 ns
Experimental dfs.media.text*Serie fss.media.text 6 ano 8 ano 129 2.608 0.010 0.061 ns
Experimental dfs.media.text*Serie fss.media.text 6 ano 9 ano 129 1.223 0.223 1.000 ns
Experimental dfs.media.text*Serie fss.media.text 7 ano 8 ano 129 1.774 0.078 0.471 ns
Experimental dfs.media.text*Serie fss.media.text 7 ano 9 ano 129 0.475 0.635 1.000 ns
Experimental dfs.media.text*Serie fss.media.text 8 ano 9 ano 129 -1.237 0.218 1.000 ns 
if (length(unique(pdat[["Serie"]])) >= 2) {
  pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
                           flow.text ~ time,
                           p.adjust.method = "bonferroni")
  lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
                                         lpwc[["grupo:Serie"]],
                                         by=c("grupo","Serie","term",".y.","group1","group2"),
                                         suffixes = c("","'"))
}
grupo Serie term .y. group1 group2 df statistic p p.adj p.adj.signif
Controle 6 ano time flow.text pre pos 260 -1.494 0.136 0.136 ns
Controle 7 ano time flow.text pre pos 260 -1.336 0.183 0.183 ns
Controle 8 ano time flow.text pre pos 260 -0.403 0.687 0.687 ns
Controle 9 ano time flow.text pre pos 260 0.066 0.947 0.947 ns
Experimental 6 ano time flow.text pre pos 260 -0.228 0.820 0.820 ns
Experimental 7 ano time flow.text pre pos 260 0.230 0.818 0.818 ns
Experimental 8 ano time flow.text pre pos 260 1.550 0.122 0.122 ns
Experimental 9 ano time flow.text pre pos 260 0.136 0.892 0.892 ns 
if (length(unique(pdat[["Serie"]])) >= 2) {
  ds <- get.descriptives(wdat, "fss.media.text", c("grupo","Serie"), covar = "dfs.media.text")
  ds <- merge(ds[ds$variable != "dfs.media.text",],
              ds[ds$variable == "dfs.media.text", !colnames(ds) %in% c("variable")],
              by = c("grupo","Serie"), all.x = T, suffixes = c("", ".dfs.media.text"))
  ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
              by = c("grupo","Serie"), suffixes = c(".emms", ""))
  ds <- ds[,c("grupo","Serie","n","mean.dfs.media.text","se.dfs.media.text",
              "mean","se","emmean","se.emms","conf.low","conf.high")]
  
  colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
                    paste0(c("M","SE"), " (unadj)"),
                    paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
  
  lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
                                          by=c("grupo","Serie"), suffixes = c("","'"))
}
grupo Serie N M (pre) SE (pre) M (unadj) SE (unadj) M (adj) SE (adj) conf.low conf.high
Controle 6 ano 31 3.269 0.119 3.488 0.107 3.528 0.099 3.332 3.724
Controle 7 ano 29 3.383 0.083 3.586 0.127 3.587 0.102 3.385 3.789
Controle 8 ano 15 3.219 0.177 3.304 0.124 3.361 0.143 3.079 3.643
Controle 9 ano 19 3.510 0.108 3.497 0.121 3.455 0.127 3.204 3.705
Experimental 6 ano 15 3.600 0.185 3.648 0.134 3.575 0.143 3.292 3.858
Experimental 7 ano 11 3.505 0.161 3.448 0.155 3.407 0.166 3.079 3.736
Experimental 8 ano 9 3.395 0.197 2.972 0.184 2.969 0.183 2.606 3.331
Experimental 9 ano 9 3.296 0.103 3.259 0.210 3.290 0.183 2.927 3.652

Plots for ancova

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "flow (textual prod)",
             subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
    ggplot2::scale_color_manual(values = color[["Serie"]]) +
    if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "flow (textual prod)",
               subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
      ggplot2::scale_color_manual(values = color[["grupo"]]) +
      if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat, "fss.media.text", c("grupo","Serie"), aov, pwcs, covar = "dfs.media.text",
    theme = "classic", color = color[["grupo:Serie"]],
    subtitle = which(aov$Effect == "grupo:Serie"))
}
if (length(unique(pdat[["Serie"]])) >= 2) {
  plots[["grupo:Serie"]] + ggplot2::ylab("flow (textual prod)") +
  ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
  if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  plots <- twoWayAncovaBoxPlots(
    wdat.long, "flow.text", c("grupo","Serie"), aov, pwc.long,
    pre.post = "time",
    theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Serie"]])) >= 2) 
  plots[["grupo:Serie"]] + ggplot2::ylab("flow (textual prod)") +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity assumption

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            facet.by = c("grupo","Serie"), add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"))
    ) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "grupo", facet.by = "Serie", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
    ggplot2::scale_color_manual(values = color[["grupo"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
  ggscatter(wdat, x = "dfs.media.text", y = "fss.media.text", size = 0.5,
            color = "Serie", facet.by = "grupo", add = "reg.line")+
    stat_regline_equation(
      aes(label =  paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
    ) +
    ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
    ggplot2::scale_color_manual(values = color[["Serie"]]) +
    if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and homogeneity

if (length(unique(pdat[["Serie"]])) >= 2) 
  res <- augment(lm(fss.media.text ~ dfs.media.text + grupo*Serie, data = wdat))
if (length(unique(pdat[["Serie"]])) >= 2)
  shapiro_test(res$.resid)
## # A tibble: 1 × 3
##   variable   statistic p.value
##   <chr>          <dbl>   <dbl>
## 1 res$.resid     0.988   0.282
if (length(unique(pdat[["Serie"]])) >= 2) 
  levene_test(res, .resid ~ grupo*Serie)
## # A tibble: 1 × 4
##     df1   df2 statistic     p
##   <int> <int>     <dbl> <dbl>
## 1     7   130     0.830 0.564

Summary of Results

Descriptive Statistics

df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"), 
                       include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
  df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
    if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
      get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
                       symmetry.test = T, normality.test = F)
    }))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
             colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
grupo Sexo Zona Cor.Raca Serie variable n mean median min max sd se ci iqr symmetry skewness kurtosis
Controle dfs.media.text 94 3.345 3.333 1.333 4.778 0.572 0.059 0.117 0.618 YES -0.490 0.954
Experimental dfs.media.text 44 3.472 3.444 1.889 4.444 0.575 0.087 0.175 0.694 YES -0.208 -0.173
dfs.media.text 138 3.385 3.389 1.333 4.778 0.574 0.049 0.097 0.667 YES -0.397 0.682
Controle fss.media.text 94 3.491 3.556 1.889 5.000 0.594 0.061 0.122 0.889 YES -0.121 -0.341
Experimental fss.media.text 44 3.380 3.354 2.333 4.667 0.586 0.088 0.178 0.833 YES 0.111 -0.751
fss.media.text 138 3.456 3.444 1.889 5.000 0.591 0.050 0.100 0.889 YES -0.045 -0.462
Controle F dfs.media.text 42 3.368 3.333 2.333 4.444 0.500 0.077 0.156 0.444 YES 0.102 -0.362
Controle M dfs.media.text 52 3.326 3.389 1.333 4.778 0.628 0.087 0.175 0.778 NO -0.677 0.956
Experimental F dfs.media.text 15 3.289 3.333 1.889 4.444 0.686 0.177 0.380 0.778 YES -0.160 -0.697
Experimental M dfs.media.text 29 3.567 3.444 2.778 4.444 0.495 0.092 0.188 0.667 YES 0.222 -1.078
Controle F fss.media.text 42 3.612 3.722 2.444 4.778 0.583 0.090 0.182 0.771 YES -0.200 -0.716
Controle M fss.media.text 52 3.393 3.444 1.889 5.000 0.589 0.082 0.164 0.889 YES -0.057 -0.091
Experimental F fss.media.text 15 3.469 3.375 2.333 4.556 0.648 0.167 0.359 1.000 YES -0.075 -1.230
Experimental M fss.media.text 29 3.334 3.333 2.333 4.667 0.558 0.104 0.212 0.778 YES 0.181 -0.550
Controle Rural dfs.media.text 51 3.345 3.333 2.111 4.778 0.577 0.081 0.162 0.667 YES 0.201 -0.340
Controle Urbana dfs.media.text 15 3.020 3.222 1.333 4.000 0.740 0.191 0.410 0.722 NO -0.797 -0.424
Experimental Rural dfs.media.text 29 3.494 3.556 1.889 4.444 0.625 0.116 0.238 0.778 YES -0.386 -0.304
Experimental Urbana dfs.media.text 6 3.444 3.444 2.889 4.333 0.544 0.222 0.571 0.639 YES 0.383 -1.448
Controle Rural fss.media.text 51 3.475 3.556 2.444 4.778 0.581 0.081 0.164 0.889 YES 0.033 -0.903
Controle Urbana fss.media.text 15 3.511 3.333 1.889 5.000 0.791 0.204 0.438 1.000 YES 0.036 -0.602
Experimental Rural fss.media.text 29 3.389 3.333 2.333 4.667 0.607 0.113 0.231 0.778 YES 0.256 -0.729
Experimental Urbana fss.media.text 6 3.377 3.299 2.889 4.111 0.453 0.185 0.476 0.538 YES 0.439 -1.520
Controle Parda dfs.media.text 45 3.421 3.333 2.444 4.444 0.466 0.069 0.140 0.556 YES 0.146 -0.364
Controle Branca dfs.media.text 9 3.173 3.222 2.111 4.222 0.674 0.225 0.518 0.667 YES -0.132 -1.303
Experimental Parda dfs.media.text 12 3.426 3.278 2.444 4.444 0.599 0.173 0.381 0.861 YES 0.177 -1.243
Experimental Branca dfs.media.text 6 3.593 3.500 2.889 4.444 0.529 0.216 0.555 0.444 YES 0.310 -1.330
Controle Parda fss.media.text 45 3.580 3.667 2.444 5.000 0.589 0.088 0.177 0.778 YES 0.122 -0.439
Controle Branca fss.media.text 9 3.358 3.556 2.556 4.000 0.538 0.179 0.414 0.778 YES -0.273 -1.734
Experimental Parda fss.media.text 12 3.130 3.111 2.333 4.111 0.556 0.161 0.353 0.861 YES 0.201 -1.374
Experimental Branca fss.media.text 6 3.444 3.444 2.889 3.889 0.391 0.160 0.411 0.528 YES -0.160 -1.905
Controle 6 ano dfs.media.text 31 3.269 3.333 2.000 4.778 0.665 0.119 0.244 0.500 YES -0.061 -0.322
Controle 7 ano dfs.media.text 29 3.383 3.333 2.556 4.333 0.449 0.083 0.171 0.667 YES 0.227 -0.711
Controle 8 ano dfs.media.text 15 3.219 3.111 1.333 4.222 0.686 0.177 0.380 0.417 NO -1.023 1.430
Controle 9 ano dfs.media.text 19 3.510 3.556 2.444 4.222 0.470 0.108 0.227 0.611 YES -0.441 -0.608
Experimental 6 ano dfs.media.text 15 3.600 3.667 1.889 4.444 0.717 0.185 0.397 1.000 NO -0.722 -0.262
Experimental 7 ano dfs.media.text 11 3.505 3.444 2.889 4.444 0.534 0.161 0.359 0.778 YES 0.240 -1.441
Experimental 8 ano dfs.media.text 9 3.395 3.444 2.444 4.333 0.591 0.197 0.454 0.444 YES 0.043 -1.170
Experimental 9 ano dfs.media.text 9 3.296 3.333 2.667 3.667 0.309 0.103 0.238 0.333 NO -0.714 -0.660
Controle 6 ano fss.media.text 31 3.488 3.556 1.889 4.556 0.595 0.107 0.218 0.778 NO -0.554 -0.027
Controle 7 ano fss.media.text 29 3.586 3.667 2.444 5.000 0.686 0.127 0.261 0.889 YES 0.085 -0.946
Controle 8 ano fss.media.text 15 3.304 3.333 2.333 4.000 0.480 0.124 0.266 0.667 YES -0.402 -0.899
Controle 9 ano fss.media.text 19 3.497 3.556 2.556 4.222 0.526 0.121 0.253 0.889 YES -0.284 -1.332
Experimental 6 ano fss.media.text 15 3.648 3.375 3.000 4.667 0.521 0.134 0.288 0.722 NO 0.631 -1.000
Experimental 7 ano fss.media.text 11 3.448 3.444 2.556 4.111 0.513 0.155 0.345 0.535 NO -0.623 -0.963
Experimental 8 ano fss.media.text 9 2.972 2.778 2.333 4.111 0.551 0.184 0.423 0.667 NO 0.781 -0.624
Experimental 9 ano fss.media.text 9 3.259 3.222 2.333 4.222 0.629 0.210 0.483 0.778 YES 0.121 -1.515

ANCOVA Table Comparison

df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
Effect DFn DFd F p p<.05 ges DFn’ DFd’ F’ p’ p<.05’ ges’
1 dfs.media.text 1 135 19.051 0.000 * 0.124 1 135 19.051 0.000 * 0.124
2 grupo 1 135 2.361 0.127 0.017 1 135 2.361 0.127 0.017
5 grupo:Sexo 1 133 0.030 0.863 0.000 1 133 0.030 0.863 0.000
6 Sexo 1 133 4.986 0.027 * 0.036 1 133 4.986 0.027 * 0.036
9 grupo:Zona 1 96 0.243 0.623 0.003 1 96 0.243 0.623 0.003
10 Zona 1 96 0.625 0.431 0.006 1 96 0.625 0.431 0.006
11 Cor.Raca 1 67 0.002 0.966 0.000 1 67 0.002 0.966 0.000
14 grupo:Cor.Raca 1 67 1.279 0.262 0.019 1 67 1.279 0.262 0.019
17 grupo:Serie 3 129 0.797 0.498 0.018 3 129 0.797 0.498 0.018
18 Serie 3 129 2.167 0.095 0.048 3 129 2.167 0.095 0.048

PairWise Table Comparison

df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
            names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
grupo Sexo Zona Cor.Raca Serie group1 group2 df statistic p p.adj p.adj.signif df’ statistic’ p’ p.adj’ p.adj.signif’
Controle pre pos 272 -1.721 0.086 0.086 ns 272 -1.721 0.086 0.086 ns
Experimental pre pos 272 0.740 0.460 0.460 ns 272 0.740 0.460 0.460 ns
Controle Experimental 135 1.537 0.127 0.127 ns 135 1.537 0.127 0.127 ns
Controle F pre pos 268 -1.924 0.055 0.055 ns 268 -1.924 0.055 0.055 ns
Controle M pre pos 268 -0.594 0.553 0.553 ns 268 -0.594 0.553 0.553 ns
Controle F M 133 1.777 0.078 0.078 ns 133 1.777 0.078 0.078 ns
Experimental F pre pos 268 -0.853 0.394 0.394 ns 268 -0.853 0.394 0.394 ns
Experimental M pre pos 268 1.529 0.127 0.127 ns 268 1.529 0.127 0.127 ns
Experimental F M 133 1.356 0.177 0.177 ns 133 1.356 0.177 0.177 ns
F Controle Experimental 133 0.681 0.497 0.497 ns 133 0.681 0.497 0.497 ns
M Controle Experimental 133 1.156 0.250 0.250 ns 133 1.156 0.250 0.250 ns
Controle Rural Urbana 96 -0.931 0.354 0.354 ns 96 -0.931 0.354 0.354 ns
Controle Rural pre pos 194 -1.068 0.287 0.287 ns 194 -1.068 0.287 0.287 ns
Controle Urbana pre pos 194 -2.180 0.030 0.030 * 194 -2.180 0.030 0.030 *
Experimental Rural Urbana 96 -0.029 0.977 0.977 ns 96 -0.029 0.977 0.977 ns
Experimental Rural pre pos 194 0.651 0.516 0.516 ns 194 0.651 0.516 0.516 ns
Experimental Urbana pre pos 194 0.189 0.851 0.851 ns 194 0.189 0.851 0.851 ns
Rural Controle Experimental 96 1.069 0.288 0.288 ns 96 1.069 0.288 0.288 ns
Urbana Controle Experimental 96 1.055 0.294 0.294 ns 96 1.055 0.294 0.294 ns
Controle Branca pre pos 136 -0.722 0.472 0.472 ns 136 -0.722 0.472 0.472 ns
Controle Parda Branca 67 0.644 0.522 0.522 ns 67 0.644 0.522 0.522 ns
Controle Parda pre pos 136 -1.390 0.167 0.167 ns 136 -1.390 0.167 0.167 ns
Experimental Branca pre pos 136 0.471 0.638 0.638 ns 136 0.471 0.638 0.638 ns
Experimental Parda Branca 67 -0.938 0.351 0.351 ns 67 -0.938 0.351 0.351 ns
Experimental Parda pre pos 136 1.333 0.185 0.185 ns 136 1.333 0.185 0.185 ns
Branca Controle Experimental 67 0.263 0.794 0.794 ns 67 0.263 0.794 0.794 ns
Parda Controle Experimental 67 2.615 0.011 0.011 * 67 2.615 0.011 0.011 *
Controle 6 ano pre pos 260 -1.494 0.136 0.136 ns 260 -1.494 0.136 0.136 ns
Controle 7 ano pre pos 260 -1.336 0.183 0.183 ns 260 -1.336 0.183 0.183 ns
Controle 8 ano pre pos 260 -0.403 0.687 0.687 ns 260 -0.403 0.687 0.687 ns
Controle 9 ano pre pos 260 0.066 0.947 0.947 ns 260 0.066 0.947 0.947 ns
Controle 6 ano 7 ano 129 -0.413 0.680 1.000 ns 129 -0.413 0.680 1.000 ns
Controle 6 ano 8 ano 129 0.969 0.335 1.000 ns 129 0.969 0.335 1.000 ns
Controle 6 ano 9 ano 129 0.455 0.650 1.000 ns 129 0.455 0.650 1.000 ns
Controle 7 ano 8 ano 129 1.291 0.199 1.000 ns 129 1.291 0.199 1.000 ns
Controle 7 ano 9 ano 129 0.814 0.417 1.000 ns 129 0.814 0.417 1.000 ns
Controle 8 ano 9 ano 129 -0.491 0.624 1.000 ns 129 -0.491 0.624 1.000 ns
Experimental 6 ano pre pos 260 -0.228 0.820 0.820 ns 260 -0.228 0.820 0.820 ns
Experimental 7 ano pre pos 260 0.230 0.818 0.818 ns 260 0.230 0.818 0.818 ns
Experimental 8 ano pre pos 260 1.550 0.122 0.122 ns 260 1.550 0.122 0.122 ns
Experimental 9 ano pre pos 260 0.136 0.892 0.892 ns 260 0.136 0.892 0.892 ns
Experimental 6 ano 7 ano 129 0.767 0.444 1.000 ns 129 0.767 0.444 1.000 ns
Experimental 6 ano 8 ano 129 2.608 0.010 0.061 ns 129 2.608 0.010 0.061 ns
Experimental 6 ano 9 ano 129 1.223 0.223 1.000 ns 129 1.223 0.223 1.000 ns
Experimental 7 ano 8 ano 129 1.774 0.078 0.471 ns 129 1.774 0.078 0.471 ns
Experimental 7 ano 9 ano 129 0.475 0.635 1.000 ns 129 0.475 0.635 1.000 ns
Experimental 8 ano 9 ano 129 -1.237 0.218 1.000 ns 129 -1.237 0.218 1.000 ns
6 ano Controle Experimental 129 -0.267 0.790 0.790 ns 129 -0.267 0.790 0.790 ns
7 ano Controle Experimental 129 0.921 0.359 0.359 ns 129 0.921 0.359 0.359 ns
8 ano Controle Experimental 129 1.687 0.094 0.094 ns 129 1.687 0.094 0.094 ns
9 ano Controle Experimental 129 0.740 0.461 0.461 ns 129 0.740 0.461 0.461 ns

EMMS Table Comparison

df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
            names(df)[!names(df) %in% names(lfatores)])]
grupo Sexo Zona Cor.Raca Serie N M (pre) SE (pre) M (unadj) SE (unadj) M (adj) SE (adj) conf.low conf.high N’ M (pre)’ SE (pre)’ M (unadj)’ SE (unadj)’ M (adj)’ SE (adj)’ conf.low’ conf.high’ N-N’
Controle 94 3.345 0.059 3.491 0.061 3.506 0.057 3.392 3.619 94 3.345 0.059 3.491 0.061 3.506 0.057 3.392 3.619 0
Experimental 44 3.472 0.087 3.380 0.088 3.349 0.084 3.183 3.515 44 3.472 0.087 3.380 0.088 3.349 0.084 3.183 3.515 0
Controle F 42 3.368 0.077 3.612 0.090 3.618 0.085 3.450 3.786 42 3.368 0.077 3.612 0.090 3.618 0.085 3.450 3.786 0
Controle M 52 3.326 0.087 3.393 0.082 3.415 0.076 3.264 3.566 52 3.326 0.087 3.393 0.082 3.415 0.076 3.264 3.566 0
Experimental F 15 3.289 0.177 3.469 0.167 3.505 0.142 3.224 3.786 15 3.289 0.177 3.469 0.167 3.505 0.142 3.224 3.786 0
Experimental M 29 3.567 0.092 3.334 0.104 3.266 0.103 3.063 3.470 29 3.567 0.092 3.334 0.104 3.266 0.103 3.063 3.470 0
Controle Rural 51 3.345 0.081 3.475 0.081 3.476 0.080 3.316 3.635 51 3.345 0.081 3.475 0.081 3.476 0.080 3.316 3.635 0
Controle Urbana 15 3.020 0.191 3.511 0.204 3.635 0.151 3.335 3.936 15 3.020 0.191 3.511 0.204 3.635 0.151 3.335 3.936 0
Experimental Rural 29 3.494 0.116 3.389 0.113 3.332 0.108 3.119 3.546 29 3.494 0.116 3.389 0.113 3.332 0.108 3.119 3.546 0
Experimental Urbana 6 3.444 0.222 3.377 0.185 3.340 0.235 2.874 3.805 6 3.444 0.222 3.377 0.185 3.340 0.235 2.874 3.805 0
Controle Branca 9 3.173 0.225 3.358 0.179 3.447 0.180 3.088 3.806 9 3.173 0.225 3.358 0.179 3.447 0.180 3.088 3.806 0
Controle Parda 45 3.421 0.069 3.580 0.088 3.574 0.079 3.416 3.733 45 3.421 0.069 3.580 0.088 3.574 0.079 3.416 3.733 0
Experimental Branca 6 3.593 0.216 3.444 0.160 3.372 0.219 2.936 3.809 6 3.593 0.216 3.444 0.160 3.372 0.219 2.936 3.809 0
Experimental Parda 12 3.426 0.173 3.130 0.161 3.122 0.154 2.814 3.429 12 3.426 0.173 3.130 0.161 3.122 0.154 2.814 3.429 0
Controle 6 ano 31 3.269 0.119 3.488 0.107 3.528 0.099 3.332 3.724 31 3.269 0.119 3.488 0.107 3.528 0.099 3.332 3.724 0
Controle 7 ano 29 3.383 0.083 3.586 0.127 3.587 0.102 3.385 3.789 29 3.383 0.083 3.586 0.127 3.587 0.102 3.385 3.789 0
Controle 8 ano 15 3.219 0.177 3.304 0.124 3.361 0.143 3.079 3.643 15 3.219 0.177 3.304 0.124 3.361 0.143 3.079 3.643 0
Controle 9 ano 19 3.510 0.108 3.497 0.121 3.455 0.127 3.204 3.705 19 3.510 0.108 3.497 0.121 3.455 0.127 3.204 3.705 0
Experimental 6 ano 15 3.600 0.185 3.648 0.134 3.575 0.143 3.292 3.858 15 3.600 0.185 3.648 0.134 3.575 0.143 3.292 3.858 0
Experimental 7 ano 11 3.505 0.161 3.448 0.155 3.407 0.166 3.079 3.736 11 3.505 0.161 3.448 0.155 3.407 0.166 3.079 3.736 0
Experimental 8 ano 9 3.395 0.197 2.972 0.184 2.969 0.183 2.606 3.331 9 3.395 0.197 2.972 0.184 2.969 0.183 2.606 3.331 0
Experimental 9 ano 9 3.296 0.103 3.259 0.210 3.290 0.183 2.927 3.652 9 3.296 0.103 3.259 0.210 3.290 0.183 2.927 3.652 0